This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Known cancer driving mutations occur in genes lying in central positions of the cancer cell signaling network. A current challenge in defining new cancer driver mutations is distinguishing between functional driver mutations among a large number of incidental passenger mutations with no functional significance in terms of tumorogenesis. Cell signaling networks can be reconstructed by various computational methods, including determination of mutual information from a wide variety of gene expression patterns. The large reconstructed network can be used to determine central players in the signaling network, using the concept of eigenvector centrality. The large adjacency matrix (20,000 x 20,000 proteins), requires significant computational resources for the calculation of eigenvecetor centrality, even when using specialized algorithms for the task. A specialized algorithm is available in the igraph package of R, and such a calculation can be carried out on a node with an extremely large memory resource.